# Pixabay Photo Metadata Search (`jungle_synthesizer/stock-photo-metadata-aggregator-scraper`) Actor Search Pixabay for photo metadata — photo ID, title, tags, photographer, license, dimensions, dominant color, CDN URL reference, and engagement stats — ready for AI datasets, content pipelines, and design tools. More sources may be added as their terms permit. - **URL**: https://apify.com/jungle\_synthesizer/stock-photo-metadata-aggregator-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** AI, Developer tools, For creators - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Pixabay Photo Metadata Search Search **Pixabay** for photo metadata. Returns photo ID, title, tags, photographer, license, dimensions, dominant color, CDN URL reference, and engagement stats — ready for AI/LLM dataset curation, content pipelines, and design tools. **Metadata only.** This actor never downloads, rehosts, or redistributes image bytes — it indexes Pixabay's own metadata plus a reference `image_cdn_url` (Pixabay's hosted CDN link) so you always attribute back to the source and license. > Only Pixabay is supported. Unsplash and Pexels are not — their Terms of Service prohibit automated > scraping/retrieval of their catalogs, so this actor never queries them. More sources may be added later > if and when their terms permit it. ### What you get - **Normalized record shape** — photo ID, title, tags, photographer name/URL, license, dimensions, dominant color, page URL, CDN reference, downloads, likes - **Attribution fields** pre-populated — photographer name, photographer profile URL, canonical photo page URL, and the Pixabay Content License string - **Search or browse** — pass a `query` to search, or leave it blank to browse Pixabay's latest/popular feed ### Input | Field | Type | Description | |-------|------|--------------| | `query` | string | Search term (e.g. `"nature"`, `"business"`, `"technology"`). Leave blank to browse editorial/popular Pixabay photos. | | `orderBy` | string | `latest` (default) or `popular`. | | `maxItems` | integer | **Required.** Maximum total records to collect. | No API key input is required — Pixabay access is handled internally. ### Output schema | Field | Description | |-------|--------------| | `source` | Always `pixabay` | | `photo_id` | Pixabay's photo ID | | `title` | First tag used as a title proxy — Pixabay doesn't expose a dedicated title field | | `description` | Pixabay's tag string | | `tags` | Comma-separated tags | | `photographer_name` | Attributed uploader name | | `photographer_url` | Uploader's Pixabay profile page | | `license` | `Pixabay Content License` | | `width` / `height` | Pixel dimensions | | `color_dominant` | Not populated — Pixabay's API doesn't expose a dominant color | | `photo_page_url` | Canonical web page — the attribution target | | `image_cdn_url` | Pixabay-hosted CDN URL, reference only — never downloaded or rehosted by this actor | | `downloads` / `likes` | Engagement counts reported by Pixabay | | `created_at` | Not populated — Pixabay's API doesn't expose an ISO creation date | ### Rate limits Paced at a safe floor under Pixabay's ~100 requests/minute limit. ### Use cases - **AI/LLM dataset curation** — build a searchable, license-tagged index of free-license photo metadata without touching image bytes - **Content pipelines** — source royalty-free imagery metadata with correct attribution fields pre-populated - **Design tooling** — power an in-house asset search UI backed by Pixabay's free-license photo metadata # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `query` (type: `string`): Search term to find photos (e.g. "nature", "business", "technology"). Leave blank to browse editorial/popular Pixabay photos. ## `orderBy` (type: `string`): Sort order for results: "latest" (default) or "popular". ## `maxItems` (type: `integer`): Maximum total number of photo records to collect. ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "query": "nature", "orderBy": "latest", "maxItems": 10 } ```` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "query": "nature", "orderBy": "latest", "maxItems": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/stock-photo-metadata-aggregator-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "query": "nature", "orderBy": "latest", "maxItems": 10, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/stock-photo-metadata-aggregator-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "query": "nature", "orderBy": "latest", "maxItems": 10 }' | apify call jungle_synthesizer/stock-photo-metadata-aggregator-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/stock-photo-metadata-aggregator-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Pixabay Photo Metadata Search", "description": "Search Pixabay for photo metadata — photo ID, title, tags, photographer, license, dimensions, dominant color, CDN URL reference, and engagement stats — ready for AI datasets, content pipelines, and design tools. More sources may be added as their terms permit.", "version": "0.1", "x-build-id": "mmM9JRFtwBdqGr0Nd" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~stock-photo-metadata-aggregator-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-stock-photo-metadata-aggregator-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/jungle_synthesizer~stock-photo-metadata-aggregator-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-stock-photo-metadata-aggregator-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/jungle_synthesizer~stock-photo-metadata-aggregator-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-stock-photo-metadata-aggregator-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "maxItems" ], "properties": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "query": { "title": "Search Query", "type": "string", "description": "Search term to find photos (e.g. \"nature\", \"business\", \"technology\"). Leave blank to browse editorial/popular Pixabay photos." }, "orderBy": { "title": "Order By", "enum": [ "latest", "popular" ], "type": "string", "description": "Sort order for results: \"latest\" (default) or \"popular\".", "default": "latest" }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum total number of photo records to collect.", "default": 50 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```